def bold_summary(querystring, summary, prefix_str="<b>", suffix_str="</b>"): ''' Bolds the portions of the querystring that are found in the summary by breaking down the querystring and summary to individual words, applying the prefix_str before, and the suffix_str after. ''' queryterms = query_parser.word_only_tokenize(querystring) summary = query_parser.word_only_tokenize(summary) for i in range(len(summary)): if summary[i] in queryterms: summary[i] = "%s%s%s" % (prefix_str, summary[i], suffix_str) return " ".join(summary)
def did_you_mean(query_string): ''' Implements a "did you mean _____" function, if the user seems to have misspelled a word. ''' original = query_string for word in query_parser.word_only_tokenize(query_string): new_word = sc.check_word(word) query_string = query_string.replace(word, new_word) return query_string if query_string != original else None
def rank_documents(querystring, document_list, database):
''' Iterates over a set of documents, and ranks them from most relevant to
least based upon the query that the user performed.
'''
docmap = {}
for doc in list(set(document_list)):
docmap[doc] = 0
for word in query_parser.word_only_tokenize(querystring):
wordweight = _inverse_term_frequency(word, database)
if wordweight == 0:
continue
for doc in docmap.keys():
docmap[doc] += wordweight * database.frequency(doc, word)
return docmap
def generate_summary(querystring, document_id, database, summary_length=20):
''' Generates a "summary" based upon the document and query string.
The summary results are weighted, so they will contain the most relevant results instead of
the greatest number of results.
'''
location_worth = {} # A map between the location, and how much the term in it is worth.
for term in query_parser.word_only_tokenize(querystring):
term_weight = _inverse_term_frequency(term, database)
for location in database.get_term_locations(term, document_id):
location_worth[location] = term_weight
locations = set(location_worth.keys())
# This could happen if the user just enters metadata or such, just give the
# initial part of the document.
if len(locations) == 0:
locations = set([0])
location_worth = {0:1}
# Search over the range, looking for the subset of terms that has the greatest
# value of its enclosed terms.
best_starting_pos = min(locations)
best_range_score = 0
for i in range(min(locations), (max(locations) - summary_length) + 1):
range_score = 0
for j in range(summary_length):
if i + j in locations:
range_score += location_worth[i + j]
if range_score > best_range_score:
best_range_score = range_score
best_starting_pos = i
return database.reconstruct_partial_document(document_id, best_starting_pos, best_starting_pos + summary_length)
